ggplot(d, aes(x=LogReactionTime)) +
geom_histogram(binwidth = .1,fill = "lightblue", color = "black") +
facet_wrap(~Task)
## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_bin()`).
ggplot(d, aes(x=LogReactionTime, fill=Task)) +
geom_density(alpha = .4)
## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_density()`).
otherwise, might not be getting the right order
d <- d %>%
# filter(Accuracy == 1) %>%
mutate(UniqueTrial = paste(ID.true,Task,WhoseList,sep="-")) %>%
separate(
ConcValCombo, # Column to split
into = c("ConcreteValue", "ValenceValue"), # New column names
sep = "-" # Separator to split at
)
# Process the data
# Process the data
output <- d %>%
group_by(UniqueTrial) %>%
mutate(
# Create WordPair using Word column values of consecutive rows
WordPair = paste0(Word, "-", lead(Word)),
# Determine SwitchCostVal by comparing ValenceValue with the next row
SwitchCostVal = case_when(
ValenceValue == lead(ValenceValue) ~ "NoSwitch",
ValenceValue != lead(ValenceValue) ~ "Switch",
TRUE ~ NA_character_ # Handle edge cases
),
# Determine SwitchCostConc by comparing ConcreteValue with the next row
SwitchCostConc = case_when(
ConcreteValue == lead(ConcreteValue) ~ "NoSwitch",
ConcreteValue != lead(ConcreteValue) ~ "Switch",
TRUE ~ NA_character_ # Handle edge cases
),
# FirstWordRT is the ReactionTime of the current row
FirstWordLogRT = LogReactionTime,
# SecondWordRT is the ReactionTime of the next row
SecondWordLogRT = lead(LogReactionTime),
# FirstWordRT is the ReactionTime of the current row
FirstWordRT = ReactionTime,
# SecondWordRT is the ReactionTime of the next row
SecondWordRT = lead(ReactionTime),
# FirstWordRT is the ReactionTime of the current row
FirstWordAccuracy = Accuracy,
# SecondWordRT is the ReactionTime of the next row
SecondWordAccuracy = lead(Accuracy),
# create the combo column
SwitchCombo = paste(SwitchCostConc,SwitchCostVal,sep='-'),
RT_Difference = SecondWordLogRT - FirstWordLogRT # Calculate the difference between RTs
) %>%
# Remove rows without valid pairings (e.g., last row in each UniqueTrial group)
filter(!is.na(lead(Word))) %>%
ungroup()
# %>%
# select(UniqueTrial, ConcreteValue, ValenceValue, WordPair, SwitchCostVal, SwitchCostConc)
# Print the resulting dataframe
table(output$SwitchCostVal)
##
## NoSwitch Switch
## 13776 14508
table(output$SwitchCostConc)
##
## NoSwitch Switch
## 13950 14334
table(output$SwitchCostConc,output$SwitchCostVal)
##
## NoSwitch Switch
## NoSwitch 6543 7407
## Switch 7233 7101
table(output$FirstWordAccuracy,output$SecondWordAccuracy)
##
## 0 1
## 0 1501 2468
## 1 2442 21873
output_acc <- output %>%
filter((FirstWordAccuracy == 1) & (SecondWordAccuracy == 1))
nrow(output_acc)/nrow(output)*100
## [1] 77.33347
# Remove subjects with ReactionTime higher than 3x IQR
summary(output_acc$SecondWordRT)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -3665.0 641.0 784.0 962.4 1037.0 90866.0
range(output_acc$SecondWordRT)
## [1] -3665 90866
hist(output_acc$SecondWordLogRT, breaks=100, col="lightblue", xlab="SecondWordLogRT (ms)",
main="Histogram with Normal Curve")
quantile(output_acc$SecondWordLogRT, na.rm = TRUE)
## 0% 25% 50% 75% 100%
## 0.6931472 6.4630295 6.6644090 6.9440872 11.4171412
# Check for missing or NaN values
sum(is.na(output_acc$SecondWordLogRT)) # Count of NA values
## [1] 1
sum(is.nan(output_acc$SecondWordLogRT)) # Count of NaN values
## [1] 0
IQR(output_acc$SecondWordLogRT, na.rm = TRUE)*3 # 0.7526289
## [1] 1.443173
cutoff.high <- quantile(output_acc$SecondWordLogRT, na.rm = TRUE)[4] + IQR(output_acc$SecondWordLogRT, na.rm = TRUE)*3 # 8.419261
cutoff.low <- quantile(output_acc$SecondWordLogRT, na.rm = TRUE)[2] - IQR(output_acc$SecondWordLogRT, na.rm = TRUE)*3# 6.5088838.419261
# remove subjects with ReactionTime higher than 3 x IQR
df.outliers.removed <- subset(output_acc, (output_acc$SecondWordLogRT > cutoff.low) & (output_acc$SecondWordLogRT < cutoff.high))
hist(df.outliers.removed$SecondWordLogRT, col="lightblue", xlab="SecondWordLogRT (ms)",
main="Histogram with Normal Curve")
agr <- df.outliers.removed %>%
group_by(WordPair,SwitchCombo) %>%
mutate(MeanSecondWordLogRT = mean(SecondWordLogRT))
ggplot(df.outliers.removed, aes(SecondWordLogRT, fill=SwitchCombo)) +
geom_density(alpha = .5)
agr <- df.outliers.removed %>%
group_by(WordPair,Task,SwitchCombo) %>%
summarize(MeanSecondWordLogRT = mean(SecondWordLogRT))
## `summarise()` has grouped output by 'WordPair', 'Task'. You can override using
## the `.groups` argument.
ggplot(agr, aes(MeanSecondWordLogRT, fill=SwitchCombo)) +
geom_density(alpha = .5) +
facet_wrap(~Task)
# Plot the RT difference as a function of SwitchCombo
ggplot(df.outliers.removed, aes(x = SwitchCombo, y = SecondWordLogRT)) +
geom_boxplot(fill = "lightblue", color = "darkblue", outlier.color = "red") +
geom_jitter(width = 0.2, alpha = 0.6) +
facet_wrap(~Task) +
labs(
title = "RT Difference by Switch Combo",
x = "Switch Combo (Valence-Concreteness)",
y = "RT Difference (ms)"
)
# theme_minimal() +
# theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Plot the RT difference as a function of SwitchCombo using a violin plot
ggplot(df.outliers.removed, aes(x = SwitchCombo, y = SecondWordLogRT, fill=Task)) +
geom_violin(fill = "lightblue", color = "darkblue", trim = FALSE) +
geom_jitter(width = 0.2, alpha = 0.6) +
facet_wrap(~Task) +
labs(
title = "RT Difference by Switch Combo",
x = "Switch Combo (Valence-Concreteness)",
y = "Log RT for second word"
)
First feature is Concreteness, second is Valence
agr <- df.outliers.removed %>%
group_by(Task,SwitchCombo,WhoseList) %>%
summarize(MeanSecondWordLogRT = mean(SecondWordLogRT),
CILow = ci.low(SecondWordLogRT),
CIHigh = ci.high(SecondWordLogRT)) %>%
mutate(YMin = MeanSecondWordLogRT - CILow,
YMax = MeanSecondWordLogRT + CIHigh)
## `summarise()` has grouped output by 'Task', 'SwitchCombo'. You can override
## using the `.groups` argument.
ggplot(agr, aes(x=Task, y=MeanSecondWordLogRT,fill=SwitchCombo)) +
geom_violin(trim=FALSE,alpha=.4) +
geom_jitter(shape=16, position=position_jitter(0.2))
# guides(fill = "none")
ggplot(agr, aes(x=SwitchCombo, y=MeanSecondWordLogRT,fill=Task)) +
geom_violin(trim=FALSE,alpha=.4) +
geom_jitter(shape=16, position=position_jitter(0.2))
agr <- df.outliers.removed %>%
group_by(Task,SwitchCombo,WhoseList) %>%
summarize(MeanSecondWordRT = mean(SecondWordRT),
CILow = ci.low(SecondWordRT),
CIHigh = ci.high(SecondWordRT)) %>%
mutate(YMin = MeanSecondWordRT - CILow,
YMax = MeanSecondWordRT + CIHigh)
## `summarise()` has grouped output by 'Task', 'SwitchCombo'. You can override
## using the `.groups` argument.
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=Task,y=MeanSecondWordRT,fill=SwitchCombo)) +
geom_bar(position=dodge,stat="identity") +
facet_wrap(~WhoseList) +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9))
# Extract the first and second words from WordPair in df.outliers.removed
df.outliers.removed$FirstWord <- sub("-.*", "", df.outliers.removed$WordPair) # Part before '-'
df.outliers.removed$SecondWord <- sub(".*-", "", df.outliers.removed$WordPair) # Part after '-'
# Match with word_features and create new columns
df.outliers.removed$FirstWordCVC <- word_features$ConcValCombo[
match(df.outliers.removed$FirstWord, word_features$Word)
]
df.outliers.removed$SecondWordCVC <- word_features$ConcValCombo[
match(df.outliers.removed$SecondWord, word_features$Word)
]
# Subset data to all columns after (and including) "UniqueTrial"
df <- df.outliers.removed[, which(names(df.outliers.removed) == "UniqueTrial"):ncol(df.outliers.removed)]
# Split df$UniqueTrial into three new columns, keeping UniqueTrial
df <- df %>%
separate(UniqueTrial, into = c("ID.true", "Task", "WhoseList"), sep = "-", remove = FALSE)
agr <- df %>%
# filter(Task == "Valence") %>%
group_by(Task,SwitchCombo,FirstWordCVC,SecondWordCVC) %>%
summarize(MeanSecondWordRT = mean(SecondWordRT),
CILow = ci.low(SecondWordRT),
CIHigh = ci.high(SecondWordRT)) %>%
mutate(YMin = MeanSecondWordRT - CILow,
YMax = MeanSecondWordRT + CIHigh)
## `summarise()` has grouped output by 'Task', 'SwitchCombo', 'FirstWordCVC'. You
## can override using the `.groups` argument.
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=SecondWordCVC,y=MeanSecondWordRT,fill=FirstWordCVC)) +
geom_bar(position=dodge,stat="identity") +
facet_wrap(~SwitchCombo) +
# facet_grid(FirstWordCVC~SecondWordCVC) +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9))
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=Task,y=MeanSecondWordRT,fill=SwitchCombo)) +
geom_bar(position=dodge,stat="identity") +
facet_grid(FirstWordCVC~SecondWordCVC) +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9)) +
labs(
# title = "Facet Grid Example",
x = "Columns = Second Word ConcValCombo",
y = "Row = First Word ConcValCombo"
)
agr <- df %>%
filter(Task == "Valence") %>%
group_by(SwitchCombo,FirstWordCVC,SecondWordCVC) %>%
summarize(MeanSecondWordRT = mean(SecondWordRT),
CILow = ci.low(SecondWordRT),
CIHigh = ci.high(SecondWordRT)) %>%
mutate(YMin = MeanSecondWordRT - CILow,
YMax = MeanSecondWordRT + CIHigh)
## `summarise()` has grouped output by 'SwitchCombo', 'FirstWordCVC'. You can
## override using the `.groups` argument.
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=SecondWordCVC,y=MeanSecondWordRT,fill=FirstWordCVC)) +
geom_bar(position=dodge,stat="identity") +
facet_wrap(~SwitchCombo) +
# facet_grid(FirstWordCVC~SecondWordCVC) +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9))
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=SwitchCombo,y=MeanSecondWordRT,fill=FirstWordCVC)) +
geom_bar(position=dodge,stat="identity") +
facet_wrap(~SecondWordCVC) +
# facet_grid(FirstWordCVC~SecondWordCVC) +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9))
agr <- df %>%
filter(Task == "Valence") %>%
group_by(WhoseList,SwitchCombo,FirstWordCVC,SecondWordCVC) %>%
summarize(MeanSecondWordRT = mean(SecondWordRT),
CILow = ci.low(SecondWordRT),
CIHigh = ci.high(SecondWordRT)) %>%
mutate(YMin = MeanSecondWordRT - CILow,
YMax = MeanSecondWordRT + CIHigh)
## `summarise()` has grouped output by 'WhoseList', 'SwitchCombo', 'FirstWordCVC'.
## You can override using the `.groups` argument.
ggplot(agr, aes(x=FirstWordCVC, y=MeanSecondWordRT,fill=SecondWordCVC)) +
geom_violin(trim=FALSE,alpha=.4) +
geom_jitter(shape=16, position=position_jitter(0.2)) +
facet_wrap(~SwitchCombo)
agr <- df %>%
filter(Task == "Valence") %>%
group_by(WhoseList,SwitchCombo,FirstWordCVC,SecondWordCVC) %>%
summarize(MeanSecondWordLogRT = mean(SecondWordLogRT),
CILow = ci.low(SecondWordLogRT),
CIHigh = ci.high(SecondWordLogRT)) %>%
mutate(YMin = MeanSecondWordLogRT - CILow,
YMax = MeanSecondWordLogRT + CIHigh)
## `summarise()` has grouped output by 'WhoseList', 'SwitchCombo', 'FirstWordCVC'.
## You can override using the `.groups` argument.
ggplot(agr, aes(x=FirstWordCVC, y=MeanSecondWordLogRT,fill=SecondWordCVC)) +
geom_violin(trim=FALSE,alpha=.4) +
geom_jitter(shape=16, position=position_jitter(0.2)) +
facet_wrap(~SwitchCombo)
agr <- df %>%
filter(Task == "Concrete") %>%
group_by(SwitchCombo,FirstWordCVC,SecondWordCVC) %>%
summarize(MeanSecondWordRT = mean(SecondWordRT),
CILow = ci.low(SecondWordRT),
CIHigh = ci.high(SecondWordRT)) %>%
mutate(YMin = MeanSecondWordRT - CILow,
YMax = MeanSecondWordRT + CIHigh)
## `summarise()` has grouped output by 'SwitchCombo', 'FirstWordCVC'. You can
## override using the `.groups` argument.
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=SecondWordCVC,y=MeanSecondWordRT,fill=FirstWordCVC)) +
geom_bar(position=dodge,stat="identity") +
facet_wrap(~SwitchCombo) +
# facet_grid(FirstWordCVC~SecondWordCVC) +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9))
agr <- df %>%
filter(Task == "Concrete") %>%
group_by(WhoseList,SwitchCombo,FirstWordCVC,SecondWordCVC) %>%
summarize(MeanSecondWordRT = mean(SecondWordRT),
CILow = ci.low(SecondWordRT),
CIHigh = ci.high(SecondWordRT)) %>%
mutate(YMin = MeanSecondWordRT - CILow,
YMax = MeanSecondWordRT + CIHigh)
## `summarise()` has grouped output by 'WhoseList', 'SwitchCombo', 'FirstWordCVC'.
## You can override using the `.groups` argument.
ggplot(agr, aes(x=SecondWordCVC, y=MeanSecondWordRT,fill=FirstWordCVC)) +
geom_violin(trim=FALSE,alpha=.4) +
geom_jitter(shape=16, position=position_jitter(0.2)) +
facet_wrap(~SwitchCombo)
agr <- df %>%
filter(Task == "Concrete") %>%
group_by(WhoseList,SwitchCombo,FirstWordCVC,SecondWordCVC) %>%
summarize(MeanSecondWordLogRT = mean(SecondWordLogRT),
CILow = ci.low(SecondWordLogRT),
CIHigh = ci.high(SecondWordLogRT)) %>%
mutate(YMin = MeanSecondWordLogRT - CILow,
YMax = MeanSecondWordLogRT + CIHigh)
## `summarise()` has grouped output by 'WhoseList', 'SwitchCombo', 'FirstWordCVC'.
## You can override using the `.groups` argument.
ggplot(agr, aes(x=SecondWordCVC, y=MeanSecondWordLogRT,fill=FirstWordCVC)) +
geom_violin(trim=FALSE,alpha=.4) +
geom_jitter(shape=16, position=position_jitter(0.2)) +
facet_wrap(~SwitchCombo)
convert everything to factors
# names(center)
m = lmer(SecondWordLogRT ~ cTask*SwitchCombo + (1|WordPair) + (1|ID.true), data=center)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SecondWordLogRT ~ cTask * SwitchCombo + (1 | WordPair) + (1 |
## ID.true)
## Data: center
##
## REML criterion at convergence: 13547.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.5260 -0.6328 -0.1627 0.4550 5.2896
##
## Random effects:
## Groups Name Variance Std.Dev.
## WordPair (Intercept) 0.004307 0.06563
## ID.true (Intercept) 0.052135 0.22833
## Residual 0.101654 0.31883
## Number of obs: 21717, groups: WordPair, 7258; ID.true, 176
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 6.750e+00 1.801e-02 1.946e+02 374.815
## cTask -1.452e-01 9.217e-03 2.146e+04 -15.755
## SwitchComboNoSwitch-Switch 3.130e-02 6.738e-03 4.972e+03 4.645
## SwitchComboSwitch-NoSwitch 3.170e-02 6.845e-03 4.891e+03 4.631
## SwitchComboSwitch-Switch 4.969e-02 6.846e-03 5.050e+03 7.259
## cTask:SwitchComboNoSwitch-Switch 4.818e-03 1.259e-02 2.145e+04 0.383
## cTask:SwitchComboSwitch-NoSwitch -6.707e-02 1.281e-02 2.139e+04 -5.237
## cTask:SwitchComboSwitch-Switch -4.706e-02 1.281e-02 2.143e+04 -3.674
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## cTask < 2e-16 ***
## SwitchComboNoSwitch-Switch 3.48e-06 ***
## SwitchComboSwitch-NoSwitch 3.74e-06 ***
## SwitchComboSwitch-Switch 4.50e-13 ***
## cTask:SwitchComboNoSwitch-Switch 0.701849
## cTask:SwitchComboSwitch-NoSwitch 1.65e-07 ***
## cTask:SwitchComboSwitch-Switch 0.000239 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cTask SCNS-S SCS-NS SwCS-S cT:SCN cT:SCS-N
## cTask 0.015
## SwtchCmNS-S -0.198 -0.031
## SwtchCmS-NS -0.194 -0.033 0.518
## SwtchCmbS-S -0.196 -0.034 0.518 0.512
## cTsk:SCNS-S -0.009 -0.719 0.025 0.023 0.023
## cTsk:SCS-NS -0.008 -0.706 0.023 0.004 0.022 0.517
## cTsk:SwCS-S -0.008 -0.704 0.023 0.022 0.012 0.518 0.510
# names(center)
m = lmer(SecondWordLogRT ~ SwitchCombo + (1|WordPair) + (1|ID.true), data=center_val)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SecondWordLogRT ~ SwitchCombo + (1 | WordPair) + (1 | ID.true)
## Data: center_val
##
## REML criterion at convergence: 4143.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.0570 -0.6095 -0.1673 0.4017 6.2554
##
## Random effects:
## Groups Name Variance Std.Dev.
## WordPair (Intercept) 0.005675 0.07533
## ID.true (Intercept) 0.062938 0.25088
## Residual 0.072624 0.26949
## Number of obs: 12297, groups: WordPair, 5837; ID.true, 151
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.681e+00 2.129e-02 1.604e+02 313.848 < 2e-16 ***
## SwitchComboNoSwitch-Switch 3.550e-02 7.779e-03 4.474e+03 4.563 5.17e-06 ***
## SwitchComboSwitch-NoSwitch 3.060e-03 7.833e-03 4.352e+03 0.391 0.696
## SwitchComboSwitch-Switch 3.167e-02 7.861e-03 4.476e+03 4.028 5.71e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) SCNS-S SCS-NS
## SwtchCmNS-S -0.196
## SwtchCmS-NS -0.194 0.531
## SwtchCmbS-S -0.194 0.531 0.527
# names(center)
m = lmer(SecondWordLogRT ~ FirstWordCVC*SecondWordCVC + (1|WordPair) + (1|ID.true), data=center_val)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SecondWordLogRT ~ FirstWordCVC * SecondWordCVC + (1 | WordPair) +
## (1 | ID.true)
## Data: center_val
##
## REML criterion at convergence: 4188.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.0415 -0.6116 -0.1685 0.4058 6.1922
##
## Random effects:
## Groups Name Variance Std.Dev.
## WordPair (Intercept) 0.005488 0.07408
## ID.true (Intercept) 0.062956 0.25091
## Residual 0.072634 0.26951
## Number of obs: 12297, groups: WordPair, 5837; ID.true, 151
##
## Fixed effects:
## Estimate
## (Intercept) 6.692e+00
## FirstWordCVCabstract-positive 1.659e-02
## FirstWordCVCconcrete-negative 2.009e-02
## FirstWordCVCconcrete-positive 4.656e-02
## SecondWordCVCabstract-positive 3.100e-02
## SecondWordCVCconcrete-negative -1.131e-02
## SecondWordCVCconcrete-positive 1.881e-02
## FirstWordCVCabstract-positive:SecondWordCVCabstract-positive -5.348e-02
## FirstWordCVCconcrete-negative:SecondWordCVCabstract-positive -2.465e-02
## FirstWordCVCconcrete-positive:SecondWordCVCabstract-positive -9.077e-02
## FirstWordCVCabstract-positive:SecondWordCVCconcrete-negative -8.102e-03
## FirstWordCVCconcrete-negative:SecondWordCVCconcrete-negative -3.028e-02
## FirstWordCVCconcrete-positive:SecondWordCVCconcrete-negative -3.400e-02
## FirstWordCVCabstract-positive:SecondWordCVCconcrete-positive -6.654e-02
## FirstWordCVCconcrete-negative:SecondWordCVCconcrete-positive 1.052e-02
## FirstWordCVCconcrete-positive:SecondWordCVCconcrete-positive -9.404e-02
## Std. Error
## (Intercept) 2.327e-02
## FirstWordCVCabstract-positive 1.456e-02
## FirstWordCVCconcrete-negative 1.508e-02
## FirstWordCVCconcrete-positive 1.602e-02
## SecondWordCVCabstract-positive 1.443e-02
## SecondWordCVCconcrete-negative 1.531e-02
## SecondWordCVCconcrete-positive 1.600e-02
## FirstWordCVCabstract-positive:SecondWordCVCabstract-positive 1.966e-02
## FirstWordCVCconcrete-negative:SecondWordCVCabstract-positive 2.050e-02
## FirstWordCVCconcrete-positive:SecondWordCVCabstract-positive 2.158e-02
## FirstWordCVCabstract-positive:SecondWordCVCconcrete-negative 2.064e-02
## FirstWordCVCconcrete-negative:SecondWordCVCconcrete-negative 2.235e-02
## FirstWordCVCconcrete-positive:SecondWordCVCconcrete-negative 2.274e-02
## FirstWordCVCabstract-positive:SecondWordCVCconcrete-positive 2.163e-02
## FirstWordCVCconcrete-negative:SecondWordCVCconcrete-positive 2.266e-02
## FirstWordCVCconcrete-positive:SecondWordCVCconcrete-positive 2.448e-02
## df t value
## (Intercept) 2.286e+02 287.535
## FirstWordCVCabstract-positive 4.442e+03 1.140
## FirstWordCVCconcrete-negative 4.296e+03 1.333
## FirstWordCVCconcrete-positive 4.369e+03 2.907
## SecondWordCVCabstract-positive 4.427e+03 2.149
## SecondWordCVCconcrete-negative 4.334e+03 -0.739
## SecondWordCVCconcrete-positive 4.437e+03 1.176
## FirstWordCVCabstract-positive:SecondWordCVCabstract-positive 4.337e+03 -2.721
## FirstWordCVCconcrete-negative:SecondWordCVCabstract-positive 4.336e+03 -1.203
## FirstWordCVCconcrete-positive:SecondWordCVCabstract-positive 4.236e+03 -4.206
## FirstWordCVCabstract-positive:SecondWordCVCconcrete-negative 4.272e+03 -0.393
## FirstWordCVCconcrete-negative:SecondWordCVCconcrete-negative 4.558e+03 -1.355
## FirstWordCVCconcrete-positive:SecondWordCVCconcrete-negative 4.321e+03 -1.495
## FirstWordCVCabstract-positive:SecondWordCVCconcrete-positive 4.338e+03 -3.076
## FirstWordCVCconcrete-negative:SecondWordCVCconcrete-positive 4.307e+03 0.464
## FirstWordCVCconcrete-positive:SecondWordCVCconcrete-positive 4.402e+03 -3.842
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## FirstWordCVCabstract-positive 0.254494
## FirstWordCVCconcrete-negative 0.182759
## FirstWordCVCconcrete-positive 0.003665 **
## SecondWordCVCabstract-positive 0.031697 *
## SecondWordCVCconcrete-negative 0.460106
## SecondWordCVCconcrete-positive 0.239688
## FirstWordCVCabstract-positive:SecondWordCVCabstract-positive 0.006543 **
## FirstWordCVCconcrete-negative:SecondWordCVCabstract-positive 0.229171
## FirstWordCVCconcrete-positive:SecondWordCVCabstract-positive 2.66e-05 ***
## FirstWordCVCabstract-positive:SecondWordCVCconcrete-negative 0.694646
## FirstWordCVCconcrete-negative:SecondWordCVCconcrete-negative 0.175508
## FirstWordCVCconcrete-positive:SecondWordCVCconcrete-negative 0.135034
## FirstWordCVCabstract-positive:SecondWordCVCconcrete-positive 0.002111 **
## FirstWordCVCconcrete-negative:SecondWordCVCconcrete-positive 0.642539
## FirstWordCVCconcrete-positive:SecondWordCVCconcrete-positive 0.000124 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 16 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
# names(center)
m = lmer(SecondWordLogRT ~ SwitchCombo + (1|WordPair) + (1|ID.true), data=center_conc)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SecondWordLogRT ~ SwitchCombo + (1 | WordPair) + (1 | ID.true)
## Data: center_conc
##
## REML criterion at convergence: 7225.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9542 -0.6593 -0.1498 0.4903 4.6421
##
## Random effects:
## Groups Name Variance Std.Dev.
## WordPair (Intercept) 0.006813 0.08254
## ID.true (Intercept) 0.073100 0.27037
## Residual 0.112249 0.33504
## Number of obs: 9420, groups: WordPair, 5064; ID.true, 157
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.820e+00 2.298e-02 1.821e+02 296.710 < 2e-16 ***
## SwitchComboNoSwitch-Switch 3.085e-02 1.047e-02 3.427e+03 2.947 0.00323 **
## SwitchComboSwitch-NoSwitch 5.927e-02 1.079e-02 3.505e+03 5.492 4.25e-08 ***
## SwitchComboSwitch-Switch 6.445e-02 1.074e-02 3.559e+03 6.001 2.16e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) SCNS-S SCS-NS
## SwtchCmNS-S -0.235
## SwtchCmS-NS -0.227 0.500
## SwtchCmbS-S -0.228 0.502 0.495
# names(center)
m = lmer(SecondWordLogRT ~ FirstWordCVC*SecondWordCVC + (1|WordPair) + (1|ID.true), data=center_val)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SecondWordLogRT ~ FirstWordCVC * SecondWordCVC + (1 | WordPair) +
## (1 | ID.true)
## Data: center_val
##
## REML criterion at convergence: 4188.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.0415 -0.6116 -0.1685 0.4058 6.1922
##
## Random effects:
## Groups Name Variance Std.Dev.
## WordPair (Intercept) 0.005488 0.07408
## ID.true (Intercept) 0.062956 0.25091
## Residual 0.072634 0.26951
## Number of obs: 12297, groups: WordPair, 5837; ID.true, 151
##
## Fixed effects:
## Estimate
## (Intercept) 6.692e+00
## FirstWordCVCabstract-positive 1.659e-02
## FirstWordCVCconcrete-negative 2.009e-02
## FirstWordCVCconcrete-positive 4.656e-02
## SecondWordCVCabstract-positive 3.100e-02
## SecondWordCVCconcrete-negative -1.131e-02
## SecondWordCVCconcrete-positive 1.881e-02
## FirstWordCVCabstract-positive:SecondWordCVCabstract-positive -5.348e-02
## FirstWordCVCconcrete-negative:SecondWordCVCabstract-positive -2.465e-02
## FirstWordCVCconcrete-positive:SecondWordCVCabstract-positive -9.077e-02
## FirstWordCVCabstract-positive:SecondWordCVCconcrete-negative -8.102e-03
## FirstWordCVCconcrete-negative:SecondWordCVCconcrete-negative -3.028e-02
## FirstWordCVCconcrete-positive:SecondWordCVCconcrete-negative -3.400e-02
## FirstWordCVCabstract-positive:SecondWordCVCconcrete-positive -6.654e-02
## FirstWordCVCconcrete-negative:SecondWordCVCconcrete-positive 1.052e-02
## FirstWordCVCconcrete-positive:SecondWordCVCconcrete-positive -9.404e-02
## Std. Error
## (Intercept) 2.327e-02
## FirstWordCVCabstract-positive 1.456e-02
## FirstWordCVCconcrete-negative 1.508e-02
## FirstWordCVCconcrete-positive 1.602e-02
## SecondWordCVCabstract-positive 1.443e-02
## SecondWordCVCconcrete-negative 1.531e-02
## SecondWordCVCconcrete-positive 1.600e-02
## FirstWordCVCabstract-positive:SecondWordCVCabstract-positive 1.966e-02
## FirstWordCVCconcrete-negative:SecondWordCVCabstract-positive 2.050e-02
## FirstWordCVCconcrete-positive:SecondWordCVCabstract-positive 2.158e-02
## FirstWordCVCabstract-positive:SecondWordCVCconcrete-negative 2.064e-02
## FirstWordCVCconcrete-negative:SecondWordCVCconcrete-negative 2.235e-02
## FirstWordCVCconcrete-positive:SecondWordCVCconcrete-negative 2.274e-02
## FirstWordCVCabstract-positive:SecondWordCVCconcrete-positive 2.163e-02
## FirstWordCVCconcrete-negative:SecondWordCVCconcrete-positive 2.266e-02
## FirstWordCVCconcrete-positive:SecondWordCVCconcrete-positive 2.448e-02
## df t value
## (Intercept) 2.286e+02 287.535
## FirstWordCVCabstract-positive 4.442e+03 1.140
## FirstWordCVCconcrete-negative 4.296e+03 1.333
## FirstWordCVCconcrete-positive 4.369e+03 2.907
## SecondWordCVCabstract-positive 4.427e+03 2.149
## SecondWordCVCconcrete-negative 4.334e+03 -0.739
## SecondWordCVCconcrete-positive 4.437e+03 1.176
## FirstWordCVCabstract-positive:SecondWordCVCabstract-positive 4.337e+03 -2.721
## FirstWordCVCconcrete-negative:SecondWordCVCabstract-positive 4.336e+03 -1.203
## FirstWordCVCconcrete-positive:SecondWordCVCabstract-positive 4.236e+03 -4.206
## FirstWordCVCabstract-positive:SecondWordCVCconcrete-negative 4.272e+03 -0.393
## FirstWordCVCconcrete-negative:SecondWordCVCconcrete-negative 4.558e+03 -1.355
## FirstWordCVCconcrete-positive:SecondWordCVCconcrete-negative 4.321e+03 -1.495
## FirstWordCVCabstract-positive:SecondWordCVCconcrete-positive 4.338e+03 -3.076
## FirstWordCVCconcrete-negative:SecondWordCVCconcrete-positive 4.307e+03 0.464
## FirstWordCVCconcrete-positive:SecondWordCVCconcrete-positive 4.402e+03 -3.842
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## FirstWordCVCabstract-positive 0.254494
## FirstWordCVCconcrete-negative 0.182759
## FirstWordCVCconcrete-positive 0.003665 **
## SecondWordCVCabstract-positive 0.031697 *
## SecondWordCVCconcrete-negative 0.460106
## SecondWordCVCconcrete-positive 0.239688
## FirstWordCVCabstract-positive:SecondWordCVCabstract-positive 0.006543 **
## FirstWordCVCconcrete-negative:SecondWordCVCabstract-positive 0.229171
## FirstWordCVCconcrete-positive:SecondWordCVCabstract-positive 2.66e-05 ***
## FirstWordCVCabstract-positive:SecondWordCVCconcrete-negative 0.694646
## FirstWordCVCconcrete-negative:SecondWordCVCconcrete-negative 0.175508
## FirstWordCVCconcrete-positive:SecondWordCVCconcrete-negative 0.135034
## FirstWordCVCabstract-positive:SecondWordCVCconcrete-positive 0.002111 **
## FirstWordCVCconcrete-negative:SecondWordCVCconcrete-positive 0.642539
## FirstWordCVCconcrete-positive:SecondWordCVCconcrete-positive 0.000124 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 16 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it